A Small Hospital’s Success in Preventing Harm with AI

“ The Jvion Machine delivers the most effective risk and intervention insights that help our caregivers identify at-risk individuals as early as possible and take the best action that will stop someone from returning to the hospital. By marrying the latest and best in cognitive science with the talented resources at Onslow Memorial Hospital, we are driving beyond our readmission goals to improve quality and thrive under at-risk models of care.

—Penney Burlingame Deal, DHA, CEO at Onslow Memorial Hospital

Client Profile: Onslow Memorial Hospital

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Onslow Memorial Hospital is a 162-bed facility founded in 1944 and located in coastal North Carolina.

The hospital offers a vast array of healthcare services with state-of-the-art diagnostic services, a modern Labor and Delivery suite with state-of-the-art monitoring system, Neonatal Intensive Care Unit, Newborn Nursery, Intensive Care Unit/Coronary Care Unit, Medical and Surgical Services, and Rehabilitation Services (including Physical, Occupational, and Speech Therapy). A responsive Emergency Department and Minor Emergency Care Unit treats more than 50,000 patients annually.

OVERVIEW

162

50,000

State-of-the-art diagnostic services

hospital beds

emergency department and minor emergency care unit patients treated annually

Challenges Key Challenges

Onslow Memorial Hospital was looking for more effective ways to reduce readmissions and improve care.

Driven in large part by the Hospital Readmission Reduction Program (HRRP), preventing readmissions within 30 days of discharge has been a key focus area for Onslow and other hospitals across the nation. Avoidable readmissions unnecessarily expose the patient to the risk of additional complications, render the provider less available for other patients, and cost patients and providers thousands, which is compounded by CMS penalties for hospitals with high 30-day readmission rates. However, the predictive capabilities of their existing MEDITECH EHR system and LACE scoring tool were insufficient to provide the patient-level insights, flexibility and scalability necessary to meet Onslow’s needs.

Additionally, Onslow sought to identify gaps in the patient experience to improve care. CMS, along with the Agency for Healthcare Research and Quality (AHRQ) developed the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey to assess a patient’s experience and perspectives on hospital care. At a high level, the key measures assessed by the survey include communication, responsiveness, pain management, care transition and planning, hospital environment, and overall rating. But the challenge with the survey is that it is retrospective. By the time the hospital finds out about gaps in a patient’s experience, they have already been discharged.

Reduce all-cause, sepsis, and pressure injury readmissions

Accurate identification of at-risk patients

Personalized interventions that address clinical and

socioeconomic risk factors

Reduce all-cause, sepsis, and pressure injury readmissions

By avoiding readmissions and the associated harm to patients, Onslow could improve both the quality of care and the cost of care. But to successfully reduce readmission rates, Onslow needed a solution that could accurately identify patients at risk, the clinical, behavioral, and social factors driving that risk, and the patient-specific interventions that would reduce that risk and prevent readmission.

Improve the quality of patient experience

The patient experience has been broadly recognized as a critical driver of overall care quality. As a result, patient-centered care has become a core tenant in efforts to improve outcomes and transform healthcare. This is exemplified by the codification of patient experience through measurement tools such as HCAHPS. By proactively identifying and addressing gaps in the patient experience, Onslow can improve patient satisfaction and engagement, facilitate better communication, ensure higher quality care, and positively impact their HCAHPS scores.

Improve the quality of patient experience

Identify and address gaps in the patient experience

Improve patient satisfation and engagement

Solution

Onslow needed an advanced solution that could account for the thousands of variables impacting a patient’s risk of readmission, and they needed something affordable and effective that could be used by practitioners at the point of care. To this end, Onslow selected the Jvion MachineTM as the system’s Artificial Intelligence (AI) enabled prescriptive analytic solution. Using a unique approach that leverages sophisticated mapping techniques, clinical data from 22 million patients, and socioeconomic-level data from government and private sources, the Jvion Machine determines a patient’s risk, the clinical and non-clinical factors driving that risk, if the patient’s risk trajectory can be changed, and—if so—

the actions that will most effectively drive to an avoided event. The output is actionable, delivering highly personalized patient-specific recommendations with the highest likelihood of improving health.

By integrating directly with Onslow’s MEDITECH EHR systeem, the Jvion Machine was able to quickly apply AI technology to Onslow’s readmission and patient experience challenges. Patient predisposition, risk, and recommended intervention information is delivered to clinicians directly through the organic workflow. The result is a seamless experience that makes the best use of advanced cognitive power and hospital resources.

Onslow selected the Jvion MachineTM as an affordable way to account for the thousands of variables impacting patients’ readmission risks

Results

Within two years of implementing the Jvion Machine, Onslow realized a sustained 20% drop in readmission occurrences, helping the hospital avoid over $3M in costs. They were also able to save 440 length-of-stay days in less than a year. In addition, Onslow’s patient experience initiative with Jvion realized improvements across all but one of the target HCAHPS metrics. Physician communication in particular,

20%drop in

readmissions

13%improvement in

physician communication

$3Msaved in costs

440length-of-stay

days saved

Within 2 years, Onslow saw real results:

a major focus at the launch of the program, improved by more than 13%. Moreover, Onslow has been able to focus resources and define new ways to promote quietness — the one dimension that did not show significant improvement. With the Jvion Machine AI, Onslow is empowered with a solution that can adapt to new demands and patient needs now and into the future.

With the Jvion Machine AI, Onslow reduced readmissions by 20% and realized improvements across all but one of the target HCAHPS metrics.

An industry-first, the Jvion MachineTM goes beyond simple predictive analytics and machine learning to identify patients on a trajectory to becoming high risk and provides clinically-validated interventions, which can be applied to 50 preventable harm conditions including sepsis, readmissions, falls, avoidable ER visits, and pressure injuries, among others. Jvion’s AI-enabled prescriptive analytics have been preventing harm across more than 40 health systems, including Cleveland Clinic, Intermountain, Grady, Sentara Healthcare, and more. For more information, visit www.jvion.com.

Learn more: jvion.com

Jvion helps healthcare systems and providers prevent harm and lower costs through AI-enabled prescriptive analytics.

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